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---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_deit_tiny_sgd_001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.88
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_10x_deit_tiny_sgd_001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2805
- Accuracy: 0.88
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5873 | 1.0 | 750 | 0.5427 | 0.8017 |
| 0.4134 | 2.0 | 1500 | 0.4078 | 0.8383 |
| 0.4003 | 3.0 | 2250 | 0.3567 | 0.8583 |
| 0.322 | 4.0 | 3000 | 0.3309 | 0.8733 |
| 0.3592 | 5.0 | 3750 | 0.3090 | 0.8767 |
| 0.2384 | 6.0 | 4500 | 0.3021 | 0.8717 |
| 0.2287 | 7.0 | 5250 | 0.2872 | 0.8833 |
| 0.2763 | 8.0 | 6000 | 0.2770 | 0.8883 |
| 0.301 | 9.0 | 6750 | 0.2801 | 0.89 |
| 0.2498 | 10.0 | 7500 | 0.2717 | 0.8933 |
| 0.2639 | 11.0 | 8250 | 0.2693 | 0.8967 |
| 0.2576 | 12.0 | 9000 | 0.2726 | 0.8967 |
| 0.2998 | 13.0 | 9750 | 0.2655 | 0.905 |
| 0.2222 | 14.0 | 10500 | 0.2676 | 0.8933 |
| 0.2757 | 15.0 | 11250 | 0.2607 | 0.8933 |
| 0.1644 | 16.0 | 12000 | 0.2662 | 0.91 |
| 0.2069 | 17.0 | 12750 | 0.2656 | 0.9033 |
| 0.2175 | 18.0 | 13500 | 0.2618 | 0.9067 |
| 0.2174 | 19.0 | 14250 | 0.2668 | 0.9 |
| 0.1626 | 20.0 | 15000 | 0.2708 | 0.8983 |
| 0.1772 | 21.0 | 15750 | 0.2632 | 0.9017 |
| 0.1739 | 22.0 | 16500 | 0.2644 | 0.9017 |
| 0.2129 | 23.0 | 17250 | 0.2644 | 0.8983 |
| 0.1768 | 24.0 | 18000 | 0.2642 | 0.8983 |
| 0.1436 | 25.0 | 18750 | 0.2692 | 0.8933 |
| 0.1864 | 26.0 | 19500 | 0.2647 | 0.8983 |
| 0.13 | 27.0 | 20250 | 0.2627 | 0.8967 |
| 0.1786 | 28.0 | 21000 | 0.2674 | 0.8967 |
| 0.1885 | 29.0 | 21750 | 0.2653 | 0.895 |
| 0.1896 | 30.0 | 22500 | 0.2757 | 0.8867 |
| 0.1887 | 31.0 | 23250 | 0.2629 | 0.8983 |
| 0.1377 | 32.0 | 24000 | 0.2703 | 0.89 |
| 0.1805 | 33.0 | 24750 | 0.2693 | 0.8917 |
| 0.1524 | 34.0 | 25500 | 0.2706 | 0.89 |
| 0.1113 | 35.0 | 26250 | 0.2737 | 0.8883 |
| 0.153 | 36.0 | 27000 | 0.2742 | 0.8867 |
| 0.1281 | 37.0 | 27750 | 0.2787 | 0.8817 |
| 0.112 | 38.0 | 28500 | 0.2764 | 0.885 |
| 0.1149 | 39.0 | 29250 | 0.2767 | 0.885 |
| 0.136 | 40.0 | 30000 | 0.2752 | 0.8833 |
| 0.1297 | 41.0 | 30750 | 0.2749 | 0.8867 |
| 0.1614 | 42.0 | 31500 | 0.2776 | 0.8833 |
| 0.1176 | 43.0 | 32250 | 0.2769 | 0.8817 |
| 0.1355 | 44.0 | 33000 | 0.2814 | 0.8817 |
| 0.1418 | 45.0 | 33750 | 0.2806 | 0.8833 |
| 0.1165 | 46.0 | 34500 | 0.2801 | 0.8817 |
| 0.1556 | 47.0 | 35250 | 0.2815 | 0.88 |
| 0.1322 | 48.0 | 36000 | 0.2803 | 0.8817 |
| 0.1369 | 49.0 | 36750 | 0.2803 | 0.8833 |
| 0.1026 | 50.0 | 37500 | 0.2805 | 0.88 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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